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11th IEEE International Conference on Tools with Artificial Intelligence
Neural Data Mining for Credit Card Fraud Detection
Chicago, Illinois
November 08-November 10
ISBN: 0-7695-0456-6
| ASCII Text | x | ||
| R. Brause, T. Langsdorf, M. Hepp, "Neural Data Mining for Credit Card Fraud Detection," 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 103, 11th IEEE International Conference on Tools with Artificial Intelligence, 1999. | |||
| BibTex | x | ||
| @article{ 10.1109/TAI.1999.809773, author = {R. Brause and T. Langsdorf and M. Hepp}, title = {Neural Data Mining for Credit Card Fraud Detection}, journal ={2012 IEEE 24th International Conference on Tools with Artificial Intelligence}, volume = {0}, year = {1999}, issn = {1082-3409}, pages = {103}, doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.1999.809773}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence TI - Neural Data Mining for Credit Card Fraud Detection SN - 1082-3409 SP EP A1 - R. Brause, A1 - T. Langsdorf, A1 - M. Hepp, PY - 1999 KW - adaptive hybrid diagnosis KW - rule generalization KW - neural networks KW - confidence increase VL - 0 JA - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence ER - | |||
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
Index Terms:
adaptive hybrid diagnosis, rule generalization, neural networks, confidence increase
Citation:
R. Brause, T. Langsdorf, M. Hepp, "Neural Data Mining for Credit Card Fraud Detection," ictai, pp.103, 11th IEEE International Conference on Tools with Artificial Intelligence, 1999
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